Incorporating AI into data science workflows is not without challenges. Data quality is an issue. There will be a lack of standards. There will be issues of model interpretability. Data that is collected from multiple sources may have a considerable amount of differences between them. Some data may be inconsistent or incomplete, leaving a lot of preprocessing that needs to happen before it can be used for AI models. Aligning the AI model with an existing data workflow may also require custom development or possibly upgrading existing infrastructure which can consume your budget and time more than what you expected. It is also vital to ensure that these AI-driven decisions are explainable and transparent, particularly in regulated industries. 











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What are the challenges of integrating AI into data science pipelines?